HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Sharma, S. K. Articles by Zhu, J. Search for Related Content PubMed Articles by Sharma, S. K. Articles by Zhu, J. Agricola Articles by Sharma, S. K. Articles by Zhu, J. Related Collections Pedotransfer Functions Water Retention/Capillary Pressure Soil Hydrology

Soil Physics

Including Topography and Vegetation Attributes for Developing Pedotransfer Functions

^a currently at Dep. of Agricultural and Food Engineering. IIT Kharagpur, West Bengal, India 721302
^b Biological and Agric. Engineering, 2117 TAMU, 301C Scoates Hall, Texas A&M Univ., College Station, TX 77843-2117
^c currently at Desert Research Institute, 755 E. Flamingo Rd., Las Vegas, NV 89119

^* Corresponding author (bmohanty{at}tamu.edu)

With the advent of advanced geographical informational systems (GIS) and remote sensing technologies in recent years, topographic (elevation, slope, aspect, and flow accumulation) and vegetation attributes are routinely available from digital elevation models (DEMs) and normalized difference vegetation index (NDVI) at different spatial (remote sensor footprint, watershed, regional) scales. Based on the correlation of soil distribution and vegetation growth patterns across a topographically heterogeneous landscape, this study explores the use of topographic and vegetation attributes in addition to pedologic attributes to develop pedotransfer functions (PTFs) for estimating soil hydraulic properties in the Southern Great Plains of the USA. The extensive Southern Great Plains 1997 (SGP97) hydrology experiment database was used to derive these functions by using artificial neural networks. Eighteen models combining bootstrapping technique with artificial neural networks were developed in a hierarchical manner to predict the soil water contents at eight different soil water potentials ( at 5, 10, 333, 500, 1000, 3000, 8000, and 15 000 cm) and the van Genuchten hydraulic parameters (_r, _s, , n). The performance of the neural network models was evaluated using the Spearman correlation coefficient between the observed and the predicted values and root mean square error (RMSE). Although variability exists within bootstrapped replications, improvements (of different levels of statistical significance) were achieved with certain input combinations of basic soil properties, topography and vegetation information compared with using only the basic soil properties as inputs. Topography (DEM) and vegetation (NDVI) attributes at finer scales were useful to capture the variations within the soil mapping units for the SGP97 region dominated by perennial grass cover.

Abbreviations: CF, Central Facility • DEMs, digital elevation models • ER, El Reno • GIS, geographical information system • LW, Little Washita • OC, organic carbon • PSD, particle size distribution • PTF, pedotransfer function • PTVTF, pedo-topo-vegetation transfer function • NDVI, normalized difference vegetation index • SGP97, Southern Great Plains 1997 hydrology experiment • SVAT, soil vegetation atmosphere transfer

This article has been cited by other articles:

				R. B. Jana, B. P. Mohanty, and E. P. Springer Multiscale Pedotransfer Functions for Soil Water Retention Vadose Zone J., November 20, 2007; 6(4): 868 - 878. [Abstract] [Full Text] [PDF]

				W. A. Agyare, S. J. Park, and P. L. G. Vlek Artificial Neural Network Estimation of Saturated Hydraulic Conductivity Vadose Zone J., May 17, 2007; 6(2): 423 - 431. [Abstract] [Full Text] [PDF]